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      "source": [
        "from torchvision import datasets\n",
        "import torch\n",
        "data_folder = '~/data/FMNIST' # This can be any directory you want to \n",
        "# download FMNIST to\n",
        "fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)\n",
        "tr_images = fmnist.data\n",
        "tr_targets = fmnist.targets"
      ],
      "execution_count": null,
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        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
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          "text": [
            "Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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          "text": [
            "\n",
            "\n",
            "Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
            "Processing...\n",
            "Done!\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "USu9lapK_520"
      },
      "source": [
        "val_fmnist = datasets.FashionMNIST(data_folder, download=True, train=False)\n",
        "val_images = val_fmnist.data\n",
        "val_targets = val_fmnist.targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oaKX_Log_7Vq"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7MkYWfNrzDsJ"
      },
      "source": [
        "### Model with 0 hidden layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZh0i54a_8zA"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()\n",
        "        x = x.view(-1,28*28)/255\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix] \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)\n",
        "\n",
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer\n",
        "\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    model.train()\n",
        "    prediction = model(x)\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "\n",
        "def accuracy(x, y, model):\n",
        "    model.eval()\n",
        "    # this is the same as @torch.no_grad \n",
        "    # at the top of function, only difference\n",
        "    # being, grad is not computed in the with scope\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pm_AtNh_9xO"
      },
      "source": [
        "def get_data(): \n",
        "    train = FMNISTDataset(tr_images, tr_targets) \n",
        "    trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
        "    val = FMNISTDataset(val_images, val_targets) \n",
        "    val_dl = DataLoader(val, batch_size=len(val_images), shuffle=False)\n",
        "    return trn_dl, val_dl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tr7aYJszAABu"
      },
      "source": [
        "@torch.no_grad()\n",
        "def val_loss(x, y, model):\n",
        "    prediction = model(x)\n",
        "    val_loss = loss_fn(prediction, y)\n",
        "    return val_loss.item()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-QsGjZhYABEm"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KKq8CFkPACLw",
        "outputId": "8abc68ee-68a9-443b-f9ff-a4dea475ee35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss) \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3SlttxNAXCM",
        "outputId": "ce4b9893-85de-4ede-d770-d9d53f8a3a38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "epochs = np.arange(5)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss with no hidden layer')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy with no hidden layer')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "frZEg63SAc7K"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHDksDDgBgp3"
      },
      "source": [
        "### Model with 1 hidden layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XyIYRbkoAdTo"
      },
      "source": [
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6ZPcquEAnOt"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SHVfTnqApUa",
        "outputId": "40ed576d-98b2-4ab1-9646-7311e265d335",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss) \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OS-HLAriArIc",
        "outputId": "6674e504-54f6-4ed1-f8b9-276586dd2fe6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "epochs = np.arange(5)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss with 1 hidden layer')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy with 1 hidden layer')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I25leQbQAuGw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q33tx2wdBrRi"
      },
      "source": [
        "Model with 2 hidden layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tasZD3fBspj"
      },
      "source": [
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCL8UX_aBxBc"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTvPNVGGBzLI",
        "outputId": "2edbce71-c797-484f-97bb-1ad251879504",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss) \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HYhXTLF2B2q5",
        "outputId": "89f4647f-cfb7-425e-b79a-dac38c65b4e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "epochs = np.arange(5)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss with 2 hidden layers')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy with 2 hidden layers')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsXD3fkYB6c8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}